Survey data analysis pattern in Data Analysis Python

Introduction

Survey data analysis helps us understand what people think or feel by looking at their answers. It turns many answers into clear insights.

You want to find out which product feature customers like the most.

You need to summarize opinions from a customer satisfaction survey.

You want to compare responses from different groups, like age or location.

You want to see the most common answers to open questions.

You want to prepare a report showing survey results with charts.

Syntax

Data Analysis Python

import pandas as pd

# Load survey data
survey_df = pd.read_csv('survey.csv')

# Check basic info
survey_df.info()

# Summarize answers for a question
summary = survey_df['Question1'].value_counts()

# Calculate average rating
average = survey_df['Rating'].mean()

# Group by category and summarize
group_summary = survey_df.groupby('AgeGroup')['Rating'].mean()

Use value_counts() to count how many times each answer appears.

Use groupby() to analyze answers by different groups.

Examples

Count how many people chose each satisfaction level.

Data Analysis Python

summary = survey_df['Satisfaction'].value_counts()

Calculate the average age of survey participants.

Data Analysis Python

average_age = survey_df['Age'].mean()

Find the average rating given by each gender group.

Data Analysis Python

grouped = survey_df.groupby('Gender')['Rating'].mean()

Sample Program

This program creates a small survey dataset, counts how many people gave each satisfaction level, calculates the average rating overall, and then finds the average rating for each age group.

Data Analysis Python

import pandas as pd

# Create sample survey data
survey_data = {
    'AgeGroup': ['18-25', '26-35', '18-25', '36-45', '26-35', '18-25'],
    'Satisfaction': ['Good', 'Excellent', 'Good', 'Poor', 'Excellent', 'Good'],
    'Rating': [4, 5, 4, 2, 5, 3]
}

survey_df = pd.DataFrame(survey_data)

# Count satisfaction answers
satisfaction_counts = survey_df['Satisfaction'].value_counts()

# Calculate average rating overall
average_rating = survey_df['Rating'].mean()

# Calculate average rating by age group
rating_by_age = survey_df.groupby('AgeGroup')['Rating'].mean()

print('Satisfaction counts:')
print(satisfaction_counts)
print('\nAverage rating overall:')
print(round(average_rating, 2))
print('\nAverage rating by age group:')
print(rating_by_age)

Important Notes

Always check your data for missing or wrong answers before analysis.

Use rounding to make average numbers easier to read.

Grouping helps compare different segments in your survey.

Summary

Survey data analysis turns many answers into clear counts and averages.

Use value_counts() for counting answers and groupby() for comparing groups.

Simple statistics like averages help summarize ratings or scores.